如何使用PyTorch将数据矩阵作为标签分配给数据集中的每个输入图像？

import torch
import torchvision
from torchvision import datasets, transforms

transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.5], std=[0.5])
])

trainset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)
testset = datasets.MNIST(root='./data', train=False, download=True, transform=transform)

trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)
testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=False)

import torch.nn as nn
import torch.optim as optim

model = YourModel()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)

for epoch in range(10):
    running_loss = 0.0
    for i, (inputs, labels) in enumerate(trainloader):
        # 将数据矩阵作为标签分配给每个输入图像
        labels = YourDataMatrix()
        
        optimizer.zero_grad()
        
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        
        running_loss += loss.item()
        if i % 100 == 99:
            print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 100))
            running_loss = 0.0
    
    # 在测试集上进行测试
    correct = 0
    total = 0
    with torch.no_grad():
        for inputs, labels in testloader:
            outputs = model(inputs)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    
    print('Accuracy on test set: %.2f%%' % (100 * correct / total))